In this blog post, we describe our work on enabling machine learning (ML) inference (aka scoring) of previously trained ML models using the newly introduced language extensions of SQL Server 2019. By implementing a set of APIs, users can interface SQL Server with an external process (such as an ML runtime in our scenario) in order to move data and results between the main execution engine and the external process (which will perform the model scoring). As we will show in this post, this method is more efficient and more intuitive than performing model scoring using SQL Server’s ML Services capability via Python scripts.
